Dynamic Data Compliance Controls Using Net-Sum Zero-Knowledge Proofs
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Solution Overview
Problem
Current data governance approaches are linear and static, failing to effectively manage dynamic data compliance across various use cases and contexts, necessitating a symbolic instruction set and zero-knowledge proof for comprehensive data compliance validation.
Innovation Solution
A software mechanism employs a symbolic instruction set to identify and apply applicable standards and directives using a net-sum formula, generating a zero-knowledge proof compliance validation key for dynamic data compliance across software, firmware, and hardware, incorporating jurisdiction identification, data enrichment, and access control processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional linear compliance mechanisms are applied to data transport and persistence, then data governance is simplified, but the system cannot adapt to dynamic data states and changing contexts
Solution Approach 1:
The patent implements dynamic compliance controls that automatically adjust based on real-time data states, contexts, and relationships. The system transitions from static linear compliance checks to dynamic evaluation that adapts to changing data conditions, enabling the same compliance mechanism to handle multiple data states and contexts effectively
Solution Approach 2:
The patent creates a universal compliance control system that can apply to multiple data states, contexts, and use cases through a single framework. The dynamic compliance mechanism serves multiple functions including transport compliance, persistence compliance, sharing compliance, and contextual compliance evaluation, replacing multiple specialized linear mechanisms
2Reliability
If detailed compliance analysis is disclosed for validation, then compliance transparency is improved, but data privacy and security are compromised
Solution Approach 1:
The patent introduces zero-knowledge proofs as an intermediary mechanism that enables compliance validation without exposing underlying data or detailed compliance analysis. The zero-knowledge proof acts as a mediator that verifies compliance claims while maintaining data privacy, allowing validators to confirm compliance reliability without accessing sensitive information
Solution Approach 2:
The patent creates cryptographic copies or representations of compliance evidence that can be validated without revealing the actual compliance details or underlying data. Instead of disclosing real compliance analysis, the system generates and validates cryptographic proofs that replicate the verification function while protecting information privacy
3Reliability
If multiple standards and directives are applied to each data element, then compliance coverage is improved, but processing time and computational overhead increase
Solution Approach 1:
The patent performs preliminary identification and tagging of applicable standards and directives before data processing operations. By pre-determining which compliance standards apply to each data element and its context, the system avoids repeated evaluation of multiple standards during processing, thereby maintaining comprehensive compliance coverage while improving processing speed
Solution Approach 2:
The patent segments the compliance evaluation process into distinct phases: identification of applicable standards, tagging of data elements with relevant compliance requirements, and validation against tagged standards. This segmentation allows the system to efficiently handle multiple standards by processing them in organized stages rather than simultaneously evaluating all standards at once
Data Source
AI summary
A software mechanism for controlling data use in compliance with applicable legal standards and directives via a symbolic instruction set that additionally creates a mathematical net-sum to provide a data compliance validation key for use of that data in software, firmware and hardware. In some embodiments, the software mechanism identifies and tags via a symbolic instruction set the standards and directives applicable to data elements as a result of laws, technical and industry standards, contractual obligations, and other sources of norms, in order to streamline data compliance in forthcoming uses of that data. In other embodiments, a symbolic instruction set creates compliance-validation keys utilizing a net-sum method across applicable data norms to provide cryptographic zero-knowledge proof of the compliance of such data for software, firmware and hardware uses.